Ti6Al4V titanium alloy is considered a biocompatible material, suitable to be used for manufacturing medical devices, particularly cranioplasty plates. Several methods for processing titanium alloys are reported in the literature, each one presenting both advantages and drawbacks. A decision-making method based upon AHP (analytic hierarchy process) was used in this paper for choosing the most recommended manufacturing process among some alternatives. The result of AHP indicated that single-point incremental forming (SPIF) at room temperature could be considered the best approach when manufacturing medical devices. However, Ti6Al4V titanium alloy is known as a low-plasticity material when subjected to plastic deformation at room temperature, so special measures had to be taken. The experimental results of processing parts from Ti6Al4V titanium alloy by means of SPIF and technological aspects are considered.
Manufacturing processes are usually complex ones, involving a significant number of parameters. Unconventional manufacturing processes, such as incremental forming is even more complex, and the establishment of some analytical relationships between parameters is difficult, largely due to the nonlinearities in the process. To overcome this drawback, artificial intelligence techniques were used to build empirical models from experimental data sets acquired from the manufacturing processes. The approach proposed in this work used an adaptive network-based fuzzy inference system to extract the value of technological force on Z-axis, which appears during incremental forming, considering a set of technological parameters (diameter of the tool, feed and incremental step) as inputs. Sets of experimental data were generated and processed by means of the proposed system, to make use of the learning ability of it to extract the empirical values of the technological force from rough data.
The paper presents a joint theoretical and experimental approach to determine the technological forces within the asymmetric single point incremental forming ASPIF process, based upon a theoretical model, image processing and data acquisition. The first step of this approach was to develop a theoretical model of the forces within the process, based upon the model of a mechanical feed drive of a CNC milling machine. By means of this model, relationships between the resistant torque at the motor spindle level and the technological force on the movement axis could be determined. Using an image processing method, which allowed the user to extract information within the machines operator panel and analytical relationships, the technological forces were determined. The results were compared with the measured values, obtained by means of a data acquisition system.
Incremental forming is a process with a high degree of novelty, so this paper describes first of all a series of theoretical principles of the incremental forming process. A main objective of the paper was the usage, as blanks in incremental forming process of bimetallic sheets. So the experimental researches have targeted the realising of parts with different shapes made from such type of blank. In the experimental researches was chosen as technological equipment for incremental forming the numerical controlled milling centre. The paper also present the tools used in incremental forming in order to obtain a hemispherical parts.
Robot manufacturing involves continuous path control, which is now available for both robotic controllers and CAM software packages. However, CAM solutions are focused on generating the code for the robotic structure to follow the toolpath, without taking into consideration the dynamics and energy consumption. In this study, robot incremental forming was considered as the manufacturing process, and a simulation model, based upon Matlab-Simulink Simscape Multibody technology, was developed. The proposed model was fed with the trajectory information generated by the CAM program, and using an inverse kinematics function, it was able to generate the commands to drive the robotic structure on the technological toolpaths. The model was also used to study the dynamic behavior of the robot; external experimental data from a 3D force sensor were fed to the model to include the influence of the technological forces which appeared during the incremental forming process. Thus, using the proposed model in conjunction with the external CAM software, the influence of the workpiece position upon the joint torques could be estimated, opening the way for future optimization. The shortcomings of the model, mainly involving inaccurate information with regard to the physical properties of the robotic structure, were addressed by subtracting the dry-run joint torques from those obtained from the technological process.
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